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The added-value of search engines is, apparently, undoubted. Their rapid evolution over the last decade has
transformed them into the most important source of information and knowledge. From the end user side,
search engine success implies correct results in fast and accurate manner, while also ranking of search
results on a given query has to be directly correlated to the user anticipated response. From the content
providers’ side (i.e. websites), better ranking in a search engine result set implies numerous advantages like
visibility, visitability, and profit. This is the main reason for the flourishing of Search Engine Optimization
(SEO) techniques, which aim towards restructuring or enriching website content, so that optimal ranking of
websites in relation to search engine results is feasible. SEO techniques are becoming more and more
sophisticated. Given that internet marketing is extensively applied, prior quality factors prove insufficient,
by themselves, to boost ranking and the improvement of the quality of website content is also introduced.
Current paper discusses such a SEO mechanism. Having identified that semantic analysis has not been
widely applied in the field of SEO, a semantic approach is adopted, which employs Latent Dirichlet
Allocation techniques coupled with Gibbs Sampling in order to analyze the results of search engines based
on given keywords. Within the context of the paper, the developed SEO mechanism LDArank is presented,
which evaluates query results through state-of-the-art SEO metrics, analyzes results’ content and extracts
new, optimized content.
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